Research on the fouling prediction of heat exchanger based on Support Vector Machine optimized by Particle Swarm Optimization algorithm

The research on the fouling prediction of heat exchanger is significantly to improve operational efficiency and economic benefits of the plants. Heat exchanger fouling prediction was introduced based on Support Vector Machine (SVM), and the Particle Swarm Optimization (PSO) was applied for optimizing the parameters of the support vector machine. One of the experiment databases of Heat exchanger fouling was used for prediction; the choosing of the parameters was also discussed. The simulations show that the precision of the PSO-SVM is better than the standard SVM in certain experiment condition and mean relative error is 0.5971%. The prediction model based on PSO-SVM offers another method for the prediction research of heat exchanger fouling.